Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297170 
Year of Publication: 
2020
Citation: 
[Journal:] Logistics Research [ISSN:] 1865-0368 [Volume:] 12 [Issue:] 1 [Article No.:] 3 [Year:] 2019 [Pages:] 1-12
Publisher: 
Bundesvereinigung Logistik (BVL), Bremen
Abstract: 
Due to changes in individual demand, manufacturing processes have become more complex and dynamic. To cope with respective fluctuations as well as machine breakdowns, capacity adjustment is one of the major effective measures. Instead of labor-oriented methods, we propose a machinery-based approach utilizing the new type of reconfigurable machine tools for adjusting capacities within a job shop system. To economically maintain desired work in process levels for all workstations, we impose a model predictive control scheme. For this method we show stability of the closed-loop for any feasible initial state of the job shop system using a terminal condition argument. For a practical application, this reduces the computation of a suitable prediction horizon to controllability of the initial state. To illustrate the effectiveness and plug-and-play availability of the proposed method, we analyze a numerical simulation of a four workstation job shop system and compare it to a state-of-the-art method.
Subjects: 
Reconfigurable machine tool
Capacity adjustment
Model predictive control
Stability
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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